قارن الطرق

	التقدير المستهدف بأقصى احتمالية (TMLE)	التقدير المتين المزدوج (AIPW)
المجال	الاستدلال السببي	الاستدلال السببي
العائلة	Machine learning	Regression model
سنة النشأة	2006	2005
صاحب الطريقة	Mark van der Laan & Daniel Rubin	Robins & Rotnitzky; Bang & Robins
النوع	Semiparametric estimator	Semiparametric causal estimator
المصدر التأسيسي	van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1).	Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
الأسماء البديلة	Targeted Learning, TMLE, Targeted MLE, Hedeflenmiş Maksimum Olabilirlik Tahmini	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
ذات صلة	3	5
الملخص	Targeted Maximum Likelihood Estimation (TMLE) is a semiparametric, doubly robust causal inference method introduced by Mark van der Laan and Daniel Rubin in 2006. It combines flexible machine learning models for both the outcome and the treatment assignment mechanism, then applies a targeting step that re-fits the initial outcome model specifically to reduce bias for a pre-specified causal estimand such as the average treatment effect. TMLE is widely used in epidemiology, biostatistics, and health economics when estimating causal effects from observational data.	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.